A hybrid deep forest-based method for predicting synergistic drug combinations

作者：Lianlian Wu, Jie Gao, Yixin Zhang, Binsheng Sui, Yuqi Wen, Qingqiang Wu, Kunhong Liu, Song He, Xiaochen Bo · 发表于：Cell Reports Methods · 年份：2023 · DOI：10.1016/j.crmeth.2023.100411 · 被引用次数：45 · 研究领域：Computational Drug Discovery Methods、vaccines and immunoinformatics approaches、Bioinformatics and Genomic Networks

Combination therapy is a promising approach in treating multiple complex diseases. However, the large search space of available drug combinations exacerbates challenge for experimental screening. To predict synergistic drug combinations in different cancer cell lines, we propose an improved deep forest-based method, ForSyn, and design two forest types embedded in ForSyn. ForSyn handles imbalanced and high-dimensional data in medium-/small-scale datasets, which are inherent characteristics of drug combination datasets. Compared with 12 state-of-the-art methods, ForSyn ranks first on four metrics for eight datasets with different feature combinations. We conduct a systematic analysis to identify the most appropriate configuration parameters. We validate the predictive value of ForSyn with cell-based experiments on several previously unexplored drug combinations. Finally, a systematic analysis of feature importance is performed on the top contributing features extracted by ForSyn. The resulting key genes may play key roles on corresponding cancers.